Instantaneous search and comparison method for large-scale distributed palm vein micro-feature data

ABSTRACT

The invention proposes an instantaneous search and comparison method for large-scale distributed palm vein micro-feature data, which consists of three parts: 1) feature extraction and calculation of palm vein micro-feature images; 2) building a feature database; 3) search and comparison. The technical solution provided by this invention, referring to the idea of GIS and web search method, is applied to the search and comparison of the palm vein micro-feature data, which enables instantaneous recognition on massive palm vein micro-feature data under large-scale, large-traffic and high-frequency application scenarios, and solves the technical problem that traditional palm vein recognition methods can not be applied to large-scale and large-traffic scenarios due to low speed thereof.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a US Patent Application of, and claims priority to,CN201910331143.3, filed Apr. 23, 2019, the disclosures of which isincorporated herein by reference in its entirety.

FIELD

The present disclosure belongs to a field of biometric featuresrecognition, relates to a technical field of computer image processingand pattern recognition, and more particularly, to an instantaneoussearch and comparison method for large-scale distributed palm veinmicro-feature data.

BACKGROUND

With the development and application of deep learning and otherartificial intelligence technologies, more and more biometric featurerecognition technologies have been developed and applied. At present,the face recognition has been widely applied. However, due to the lackof differentiated features between different individuals, no matter howpowerful the algorithm is, it is still difficult to distinguish twopeople with similar looks, such as twins. Therefore, the facerecognition technology is difficult to be applied to the identificationof individuals in super large-scale population. The palm veinrecognition is a technology that uses micro-features of veindistribution inside the palm of the human body for identification, whichbelongs to a living body recognition of internal features, cannot becounterfeited or forged, thus has a high security level and containsvast differentiation information features between individuals, and canbe applied to large traffic and large-scale application scenarios.

In the past, since the computing performance of computer is insufficientand the excellent artificial intelligence algorithm is not powerfulenough, how to deal with the instantaneous comparison of large-scale andmassive palm vein micro-feature data has become a difficult problem inthe development of the palm vein recognition technology. Theconventional palm vein recognition has many limitations. Although therecognition accuracy is high, the recognition speed is slow and it isdifficult to meet the large traffic and high frequency usagerequirements. The present disclosure provides an instantaneous searchand comparison method for large-scale distributed palm veinmicro-feature data, which not only utilizes the vein distributionfeatures of the whole palm, but also adopts the method of combining theglobal feature vector index and the local feature vector index of thedeep learning, and introduces the inverted index method widely used innetwork search engine, to overcome the technical difficulty of theconventional palm vein recognition, and to break through the bottleneckof slow comparison speed. Through the technical solution, the problemthat the recognition speed of palm vein recognition is slow under thelarge traffic and large-scale application scenarios can be solved, andinstantaneous recognition with ultra-large traffic and ultra-highprecision is realized.

SUMMARY

As for the above existing problems, the present disclosure provides aninstantaneous search and comparison method for large-scale distributedpalm vein micro-feature data, using for reference of the idea of MapZoom Level in GIS (Geographic Information System), the Map Zoom Level isranged from 0 to 8 layers like a pyramid structure. The top layer 8 onlyhas the thickest vein information, and as the number of layersdecreases, the details gradually increase, thereby forming a sequence.Since the image of the bottom layer 0 has plenitude details, which needsto be segmented, then the convolution neural network is respectivelyapplied to each layer and the segmented plate images to obtain a globaland local feature vector, these vectors are categorized respectively byadopting a clustering algorithm, so as to form a global feature vectorindex and a local feature vector index, as well as a global and a localinverted index. Similar to the web multi-keyword search of a searchengine, the vectors of each layer of Zoom level and the vector of thelocal plate at the bottom layer are each equivalent to a web keyword,candidate target web pages are the intersection and union of the searchresults of these keywords. Therefore, the search comparison of palm veinmicro-feature data is similar, but more complex than the web searchengine. Like PageRank of the web search, palm vein micro-feature datacompare also needs to calculate the fraction according to thesimilarity, and take the feature vector with the highest similarityfraction as the final matched target.

To achieve the above-mentioned objective, the present disclosureprovides an instantaneous search and comparison method for large-scaledistributed palm vein micro-feature data, specifically including:

S1: feature extraction and calculation of a palm vein micro-featureimage, specifically comprises:

S1-1: building multi-scale palm vein feature spaces, let the originalimage be L₀, building a downsample image pyramid sequence Sp={L₀, L₁, .. . , L_(n-1)}, where n is the total image number, c is the downsamplefactor;

S1-2: palm vein image enhancement, implement multi-scale Gauss filterson each layer of the sequence Sp, then extract the vein on the filteredimages, combine the vein images under different scale kernels, obtainingthe vein image sequence {V₀, V₁, . . . , V_(n-1)};

S1-3: extract the global and local features of the vein image sequence,combine the global and local features together to get the final featureF;

S2: building feature database, specifically comprises:

S2-1: bucket partition on the global features from the feature dataset,initialize z buckets randomly, classifying each global feature usingcluster algorithm;

S2-2: building an inverted index for the local feature, building a“local feature ID—user ID” pair for the local feature of each feature F,building index system for all the local features, grouping the localfeatures that are close in distance as one class, and putting the userIDs with the same local feature class to the inverted recording list;

S3: search and comparison, specifically comprises:

S3-1: introduce the feature H to be compared;

S3-2: get the bucket index sequence {id₀, id₁, . . . , id_(p-1)} byusing the p global features H₀ of the feature H, then implement thesearch in the acquired p buckets parallelly, the searching process ineach bucket comprises:

-   -   1) traverse all the local features of the feature H, find the t        nearest neighbors of each local feature;    -   2) find the set T_(m) of user IDs corresponding to the t        neighbors;    -   3) implement the intersection operation on all the IDs acquired        from the m local features:        G _(id) =T ₀ ∩T ₁ ∩ . . . ∩T _(m-1) ={ID ₀ ,ID ₁ , . . . ,ID        _(j)}

obtaining the ID set of each bucket;

S3-3: implement the union operation on ID set of each bucket, i.e.,G=G ₀ ∪G ₁ ∪ . . . G _(p-1) ={g ₀ ,g ₁ , . . . ,g _(j)}

get the final ID set G;

S3-4: calculate the similarity score of feature H with each feature inset G;

$s = \frac{\sum\limits_{i = 0}^{m - 1}{{{vg}_{i} - {vp_{i}}}}}{m}$

where, vg is the local feature vector of G, vp is the local featurevector of H, m is the number of local features;

S3-5: select the one with the highest similarity score as the finalmatched target.

The technical solution provided by the present disclosure can implementinstant comparison and recognition of mass palm vein micro-feature underlarge-scale and large-traffic application scenarios, which overcomes theproblem that the speed of conventional palm vein recognition in massdata comparison and recognition is slow, and breaks through thedevelopment bottleneck of palm vein recognition technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of a technical solution of the presentdisclosure;

FIG. 2 is a schematic diagram of generating a vein image sequence of thepresent disclosure;

FIG. 3 is a schematic diagram of extracting a global feature vector ofthe present disclosure;

FIG. 4 is a network structure diagram of extracting a global featurevector of the present disclosure;

FIG. 5 is a Layer structure schematic diagram of a convolutional neuralnetwork of the present disclosure;

FIG. 6 is a Block structure schematic diagram of a convolutional neuralnetwork of the present disclosure;

FIG. 7 is a schematic diagram of extracting a local feature vector ofthe present disclosure;

FIG. 8 is a network structure diagram of extracting a local featurevector of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In order to make the objective and technical solution of the presentdisclosure clearer, the present disclosure will be furthermore describedin detail and completely below with reference to the accompanyingdrawings. It should be appreciated that, the specific embodimentsdescribed herein is merely for the purpose of explanation of thetechnical solution of the present disclosure, other embodiments obtainedby those skilled in the art without making creative effort shall fallwithin the protection scope of the present disclosure.

An instantaneous search and comparison method for large-scaledistributed palm vein micro-feature data, which consists of threeparts: 1) feature extraction and calculation of palm vein micro-featureimages; 2) building a feature database; 3) search and comparison, asshown in FIG. 1, specific implemented steps are given as follows.

S1: feature extraction and calculation of a palm vein micro-featureimage, specifically comprises:

S1-1: building multi-scale palm vein feature spaces, let the originalimage be L₀, building a downsample image pyramid sequence Sp={L₀, L₁, .. . , L_(n-1)}, where n is the total image number, c is the downsamplefactor;

S1-2: palm vein image enhancement, implement multi-scale Gauss filterson each layer of the sequence Sp, then extract the vein on the filteredimages, combine the vein images under different scale kernels, obtainingthe vein image sequence {V₀, V₁, . . . , V_(n-1)}, as shown in FIG. 2which is a schematic diagram of generating a vein image sequence.

Furthermore, the multi-scale Gauss filters specifically refers to:Z _(i) ^(k)(x,y)=G(x,y,kσ)*L _(i)(x,y)

L_(i)(x,y) denotes the input image, Z_(i) ^(k)(x,y) denotes the outputimage, * is the convolution operator, subscript i denotes the imagesequence index, G(x,y,kσ) is the Gauss filter kernel where:

${G\left( {x,y,{k\;\sigma}} \right)} = {\frac{1}{2{\pi\left( {k\;\sigma} \right)}^{2}}{\exp\left( {- \frac{x^{2} + y^{2}}{2\left( {k\sigma} \right)^{2}}} \right)}}$

k is the scale parameter of the Gauss filer kernel, and the parameterk∈K={1,2,3}.

Furthermore, the step of extracting the vein on the filtered images andcombining the vein images under different scale kernels are specificallyimplemented through the following steps.

First, performing vein splitting by using Phase Stretch Transformalgorithm,A[x,y]=∠

IFFT2{{tilde over (K)}[α,β]·FFT2{B[x,y]}}

where, B[x,y] is the input image, A[x,y] is the output image, ∠<⋅> isthe angle operator, FFT2 is the two-dimensional fast fourier transform,IFFT2 is the inverse two-dimensional fast fourier transform, α and β arethe two-dimensional frequency variables, {tilde over (K)}[α,β] is aphase distortion kernel function, i.e.,

${\overset{\sim}{K}\left\lbrack {\alpha,\beta} \right\rbrack} = e^{j \cdot {\varphi{\lbrack{\alpha,\beta}\rbrack}}}$$\begin{matrix}{{\varphi\left\lbrack {\alpha,\beta} \right\rbrack} = {\varphi_{polar}\left\lbrack {r,\theta} \right\rbrack}} \\{= {\varphi_{polar}\lbrack r\rbrack}} \\{= {S \cdot \frac{{W \cdot r \cdot {\tan^{- 1}\left( {W \cdot r} \right)}} - {\left( {1/2} \right) \cdot {\ln\left( {1 + \left( {W \cdot r} \right)^{2}} \right)}}}{{W \cdot r_{\max} \cdot {\tan^{- 1}\left( {W \cdot r_{\max}} \right)}} - {\left( {1/2} \right) \cdot {\ln\left( {1 + \left( {W \cdot r_{\max}} \right)^{2}} \right)}}}}}\end{matrix}$

where, r=√{square root over (α²+β²)}, the final value of φ is irrelevantto θ, thus can be omitted directly, r_(max) is the maximum value of r, Sis the strength coefficient, and W is the distortion strength, thevalues of S and W depend on the image.

Second, merging the vein images under different scale kernels,

$V_{i} = {\underset{k}{merge}\left( V_{i}^{k} \right)}$

where, the merging operation consists of the following steps:

a) binarize V_(i) ^(k);

b) calculate the summation of the binarized V_(i) ^(k) in correspondingposition, i.e.,

${V_{i}\left( {x,y} \right)} = {\sum\limits_{k}{V_{i}^{k}\left( {x,y} \right)}}$

c) perform the region extremum growth on the image V_(i), i.e.,

${v\left( {x,y} \right)} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu}{v\left( {x,y} \right)}} = 0} \\\left( {v\left( {x,y} \right)} \right. & {{{if}\mspace{14mu}{v\left( {x,y} \right)}} \neq {0\mspace{14mu}{and}\mspace{14mu} v_{\max\text{-}{neighbor}}} \leq {thr}} \\v_{\max\text{-}{neighbor}} & {{{if}\mspace{14mu}{v\left( {x,y} \right)}} \neq {0\mspace{14mu}{and}\mspace{14mu} v_{\max\text{-}{neighbor}}} > {thr}}\end{matrix} \right.$

where, v_(max-neighbor) is the maximum value in the region, thr is anexperience threshold, and generating the vein image sequence {V₀, V₁, .. . , V_(n-1)} after merging.

S1-3: extract the global and local features of the vein image sequence,combine the global and local features together to get the final featureF.

Furthermore, the step of extracting the global and local features of thevein image sequence specifically includes the following step:

select the first p sequence images O={V₀, V₁, . . . , V_(p-1)} from thevein image sequence {V₀, V₁, . . . , V_(n-1)} for the global featurecalculation, the remaining images used for the local featurecalculation.

As shown in FIG. 3, the network used for the global feature calculationis GlobalNet; primary vein extraction on the sequence images O one byone, resize the primary vein sequence images to the same size and inputthem to the network GlobalNet, generating the feature vector sequence:F _(O) ={f ₀ ,f ₁ , . . . ,f _(p-1)}.

As shown in FIG. 4, the global feature vector extraction network(GlobalNet) mainly consists of four layers, each layer consists ofmultiple Blocks. Layer 1 has 3 Blocks, Layer 2 has 4 Blocks, Layer 3 has23 Blocks, and Layer 4 has 3 Blocks. The output of Layer 4 generatesglobal feature vectors through AvgPool layer. As shown in FIG. 5 whichis a Layer structure schematic diagram of a convolutional neural networkof the present disclosure, and FIG. 6 shows a Block structure schematicdiagram of a convolutional neural networks of the present disclosure.

As shown in FIG. 7, for the local feature, region segmentation on thevein image and segmentation on the remaining images are required for thelocal feature calculation, suppose the number of remaining images is q,the whole palm is divided into m local regions, for each local region,input the local vein image into the network LocalNet and generate thelocal feature vector v_(m) ^(q), where the superscript q denotes thesequence index, subscript m denotes the local feature index in the wholepalm, subsequently, merging the local features in the sequence with thesame position, i.e.,

$v_{m} = \frac{\sum\limits_{i = 0}^{q - 1}v_{m}^{i}}{q}$

get the final local feature F_(l)={v₀, v₁, . . . , v_(m-1)};

combine the global and local features together to obtain the finalfeature F, i.e.,F=(p,m,F _(o) ,F _(l))

As shown in FIG. 8, the local feature vector extraction network(LocalNet) also consists of 4 layers, the number of Block of each layeris 3, 4, 6 and 3 in sequence. The output of Layer 4 generates the localfeature vector through Convolution.

S2: building feature database, specifically comprises:

S2-1: bucket partition on the global features from the feature dataset,initialize z buckets randomly, classifying each global feature usingcluster algorithm;

Furthermore, the step of classifying each global feature using clusteralgorithm specifically includes:

for the feature F, since there are p groups of global features,generating partition vector D={id₀, id₁, . . . , id_(p-1)}, where id_(k)denotes the bucket index of the class k+1, counting the class indexassigned to the feature F, the feature F will be considered to be theclass with the most assignments, if all the assignments are 1, selectthe first group with index 0 as the feature F class id.

S2-2: building an inverted index for the local feature, building a“local feature ID—user ID” pair for the local feature of each feature F,building index system for all the local features, grouping the localfeatures that are close in distance as one class, and putting the userIDs with the same local feature class to the inverted recording list.

S3: search and comparison, specifically comprises:

S3-1: introduce the feature H to be compared;

S3-2: get the bucket index sequence {id₀, id₁, . . . , id_(p-1)} byusing the p global features H₀ of the feature H, then implement thesearch in the acquired p buckets parallelly, the searching process ineach bucket comprises:

1) traverse all the local features of the feature H, find the t nearestneighbors of each local feature;

2) find the set T_(m) of user IDs corresponding to the t neighbors;

3) implement the intersection operation on all the IDs acquired from them local features:G _(id) =T ₀ ∩T ₁ ∩ . . . ∩T _(m-1) ={ID ₀ ,ID ₁ , . . . ,ID _(j)}

obtaining the ID set of each bucket;

S3-3: implement the union operation on ID set of each bucket, i.e.,G=G ₀ ∪G ₁ ∪ . . . G _(p-1) ={g ₀ ,g ₁ , . . . ,g _(j)}

get the final ID set G;

S3-4: calculate the similarity score of feature H with each feature inset G;

$s = \frac{\sum\limits_{i = 0}^{m - 1}{{{vg}_{i} - {vp_{i}}}}}{m}$

where, vg is the local feature vector of G, vp is the local featurevector of H, m is the number of local features;

S3-5: select the one with the highest similarity score as the finalmatched target.

Therefore, finally the feature vector with the highest similarityfraction is the final matched target, and the user ID corresponding tothe feature vector is the final result of the comparison andrecognition. The technical solution provided by the present disclosure,using for reference of the idea of GIS and the method of web search, isapplied to the search comparison of palm vein micro-feature data, canachieve instantaneous comparison and recognition of mass palm veinmicro-feature data in the large-scale, large-traffic and high-frequencyapplication scenarios, and solve the technical problem that theconventional palm vein recognition cannot be applied in large-scale andlarge-traffic due to its low speed.

The above-mentioned content can be implemented by those skilled in theart, any modification, equivalent replacement made without departingfrom the concept of the technical solution of the present disclosureshall all fall within the protection scope of the present disclosure.

The invention claimed is:
 1. An instantaneous search and comparisonmethod for large-scale distributed palm vein micro-feature data,includes the following steps: S1: feature extraction and calculation ofa palm vein micro-feature image, specifically comprises: S1-1: buildingmulti-scale palm vein feature spaces, let the original image be L₀,building a downsample image pyramid sequence Sp={L₀, L₁, . . . ,L_(n-1)}, where n is the total image number, c is the downsample factor;S1-2: palm vein image enhancement, implement multi-scale Gauss filterson each layer of the sequence Sp, then extract the vein on the filteredimages, combine the vein images under different scale kernels, obtainingthe vein image sequence {V₀, V₁, . . . , V_(n-1)}; S1-3: extract theglobal and local features of the vein image sequence, combine the globaland local features together to get the final feature F; S2: buildingfeature database, specifically comprises: S2-1: bucket partition on theglobal features from the feature dataset, initialize z buckets randomly,classifying each global feature using cluster algorithm; S2-2: buildingan inverted index for the local feature, building a “local featureID—user ID” pair for the local feature of each feature F, building indexsystem for all the local features, grouping the local features that areclose in distance as one class, and putting the user IDs with the samelocal feature class to the inverted recording list; S3: search andcomparison, specifically comprises: S3-1: introduce the feature H to becompared; S3-2: get the bucket index sequence {id₀, id₁, . . . ,id_(p-1)} by using the p global features H₀ of the feature H, thenimplement the search in the acquired p buckets parallelly, the searchingprocess in each bucket comprises: 1) traverse all the local features ofthe feature H, find the t nearest neighbors of each local feature; 2)find the set T_(m) of user IDs corresponding to the t neighbors; 3)implement the intersection operation on all the IDs acquired from the mlocal features:G _(id) =T ₀ ∩T ₁ ∩ . . . ∩T _(m-1) ={ID ₀ ,ID ₁ , . . . ,ID _(j)}obtaining the ID set of each bucket; S3-3: implement the union operationon ID set of each bucket, i.e.,G=G ₀ ∪G ₁ ∪ . . . G _(p-1) ={g ₀ ,g ₁ , . . . ,g _(j)} get the final IDset G; S3-4: calculate the similarity score of feature H with eachfeature in set G;$s = \frac{\sum\limits_{i = 0}^{m - 1}{{{vg}_{i} - {vp_{i}}}}}{m}$where, vg is the local feature vector of G, vp is the local featurevector of H, m is the number of local features; S3-5: select the onewith the highest similarity score as the final matched target.
 2. Theinstantaneous search and comparison method for large-scale distributedpalm vein micro-feature data according to claim 1, the multi-scale Gaussfilters in step S1-2 are:Z _(i) ^(k)(x,y)=G(x,y,kσ)*L _(i)(x,y) L_(i)(x,y) denotes the inputimage, Z_(i) ^(k)(x,y) denotes the output image, * is the convolutionoperator, subscript i denotes the image sequence index, G(x,y,kσ) is theGauss filter kernel, where:${G\left( {x,y,{k\;\sigma}} \right)} = {\frac{1}{2{\pi\left( {k\;\sigma} \right)}^{2}}{\exp\left( {- \frac{x^{2} + y^{2}}{2\left( {k\sigma} \right)^{2}}} \right)}}$k is the scale parameter of the Gauss filer kernel, and the parameterk∈K={1,2,3}.
 3. The instantaneous search and comparison method forlarge-scale distributed palm vein micro-feature data according to claim1, extract vein on filtered image, and then combine the vein imagesunder different scale kernels in the S1-2, specifically comprises:first, performing vein splitting by using Phase Stretch Transformalgorithm,A[x,y]=∠

IFFT2{{tilde over (K)}[α,β]·FFT2{B[x,y]}}

where, B[x,y] is the input image, A[x,y] is the output image, ∠<⋅> isthe angle operator, FFT2 is the two-dimensional fast fourier transform,IFFT2 is the inverse two-dimensional fast fourier transform, α and β arethe two-dimensional frequency variables, {tilde over (K)}[α,β] is aphase distortion kernel function, i.e.,${\overset{\sim}{K}\left\lbrack {\alpha,\beta} \right\rbrack} = e^{j \cdot {\varphi{\lbrack{\alpha,\beta}\rbrack}}}$$\begin{matrix}{{\varphi\left\lbrack {\alpha,\beta} \right\rbrack} = {\varphi_{polar}\left\lbrack {r,\theta} \right\rbrack}} \\{= {\varphi_{polar}\lbrack r\rbrack}} \\{= {S \cdot \frac{{W \cdot r \cdot {\tan^{- 1}\left( {W \cdot r} \right)}} - {\left( {1/2} \right) \cdot {\ln\left( {1 + \left( {W \cdot r} \right)^{2}} \right)}}}{{W \cdot r_{\max} \cdot {\tan^{- 1}\left( {W \cdot r_{\max}} \right)}} - {\left( {1/2} \right) \cdot {\ln\left( {1 + \left( {W \cdot r_{\max}} \right)^{2}} \right)}}}}}\end{matrix}$ where, r=√{square root over (α²+β²)}, the final value of φis irrelevant to θ, thus can be omitted directly, r_(max) is the maximumvalue of r, S is the strength coefficient, and W is the distortionstrength, the values of S and W depend on the image; second, merging thevein images under different scale kernels,$V_{i} = {\underset{k}{merge}\left( V_{i}^{k} \right)}$ where, themerging operation consists of the following steps: a) binarize V_(i)^(k); b) calculate the summation of the binarized V_(i) ^(k) incorresponding position, i.e.,${V_{i}\left( {x,y} \right)} = {\sum\limits_{k}{V_{i}^{k}\left( {x,y} \right)}}$c) perform the region extremum growth on the image V_(i), i.e.,${v\left( {x,y} \right)} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu}{v\left( {x,y} \right)}} = 0} \\{v\left( {x,y} \right)} & {{{if}\mspace{14mu}{v\left( {x,y} \right)}} \neq {0\mspace{14mu}{and}\mspace{14mu} v_{\max\text{-}{neighbor}}} \leq {thr}} \\v_{\max\text{-}{neighbor}} & {{{if}\mspace{14mu}{v\left( {x,y} \right)}} \neq {0\mspace{14mu}{and}\mspace{14mu} v_{\max\text{-}{neighbor}}} > {thr}}\end{matrix} \right.$ Where, v_(max-neighbor) is the maximum value inthe region, thr is an experience threshold, and generating the veinimage sequence (V₀, V₁, . . . , V_(n-1)) after merging.
 4. Theinstantaneous search and comparison method for large-scale distributedpalm vein micro-feature data according to claim 1, the global and localfeatures extraction for vein image sequence in S1-3, specificallycomprises: select the first p sequence images O={V₀, V₁, . . . ,V_(p-1)} from the vein image sequence {V₀, V₁, . . . , V_(n-1)} for theglobal feature calculation, the remaining images used for the localfeature calculation; the network used for the global feature calculationis GlobalNet; primary vein extraction on the sequence images O one byone, resize the primary vein sequence images to the same size and inputthem to the network GlobalNet, generating the feature vector sequence:F _(O) ={f ₀ ,f ₁ , . . . ,f _(p-1)}; region segmentation on the veinimage and segmentation on the remaining images are required for thelocal feature calculation, suppose the number of remaining images is q,the whole palm is divided into m local regions, for each local region,input the local vein image into the network LocalNet and generate thelocal feature vector v_(m) ^(q), where the superscript q denotes thesequence index, subscript m denotes the local feature index in the wholepalm, subsequently, merging the local features in the sequence with thesame position, i.e.,$v_{m} = \frac{\sum\limits_{i = 0}^{q - 1}v_{m}^{i}}{q}$ get the finallocal feature F_(l)={v₀, v₁, . . . , v_(m-1)}; combine the global andlocal features together to obtain the final feature F, i.e.,F=(p,m,F _(o) ,F _(l)).
 5. The instantaneous search and comparisonmethod for large-scale distributed palm vein micro-feature dataaccording to claim 1, classify each global feature using a clusteralgorithm in S2-1, specifically comprises: for the feature F, sincethere are p groups of global features, generating partition vectorD={id₀, id₁, . . . , id_(p-1)}, where id denotes the bucket index of theclass k+1, counting the class index assigned to the feature F, thefeature F will be considered to be the class with the most assignments,if all the assignments are 1, select the first group with index 0 as thefeature F class id.